Analytics and monitoring decision
Mad Dog Alpha AI
A single developer can build a narrow AI report generator (data ingestion + LLM + UI) in about a week, but reproducing a full commercial equity-research product (curated data licenses, proprietary models, polished UX, and integrations) is unlikely without additional resources.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off40 h to build
$200/mo3 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Mad Dog Alpha AI alternatives, with the arithmetic →
What a replacement has to do
- Ingest market and company data, run an LLM to generate research and summaries, store and index reports, and serve them via a web UI with simple account access
What it still won’t have
- proprietary data licensing and curated market feeds
- any proprietary or fine-tuned models the vendor may use
- production polish, dashboards, and polished UX
- broad integrations (brokerage, research distribution) and scale infrastructure
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Mad Dog Alpha AI does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
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What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal AI equity-research web app using Node.js (Express) backend, Postgres for storage, a React frontend, and an LLM API (e.g., OpenAI) for report generation. Core features: 1) fetch and normalize price and fundamentals from public APIs, 2) prompt the LLM to produce an executive summary and structured report for a single ticker on demand, 3) store raw inputs, prompts, and generated reports in Postgres with full-text search, 4) a React UI to request reports, view history, and trigger refresh, 5) a scheduled job to refresh data and optionally regenerate reports. Out of scope: fine-tuning proprietary models, paid market data licensing, multi-user billing, broker integrations, and advanced analytics dashboards. Include error handling for API failures, retries for jobs, unit tests for core data and generation logic, and end-to-end tests for the report flow.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 3 cited sources+3
- 3/3 assessment runs agreed+4
- Evidence score64
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productMad Dog Alpha — AI Equity Research
- open sourceFinceptTerminal repository
- open sourcequantstats repository
Integrity checks
What held up, and what did not.
